Using fMRI signal directly for voxel based decoding will introduce considerable noise and easily cause overfitting due to high data dimensionality, small sample size and low signal-to-noise ratio [1]}, [2]}, [3]}. Therefore, we first use brain atlas [4]}, [5]} to divide the whole brain cortical into multiple brain areas (ROIs). In order to further extract the features of each ROI, we place the voxels of each ROI in a 3-D volume according to its coordinates, then split the volume evenly into several subvolumes and calculate the average brain activity of voxels in each subvolume as the feature of this subvolume as illustrated in Fig. REF  (take 8 sub-volumes for example). Then we concatenate the features of ROI in each hemisphere to obtain ROI pooling features \(\mathbf {x}^l\)  and \(\mathbf {x}^r\) . This method can realize noise suppression and dimensionality reduction of the input signal. We set the number of features in each ROI as an optional hyperparameter named \(N_{ROIF}\) .
